arXiv Machine Learning

BanglaMamba: Exploring State Space Models for Bangla Fake News Detection

BanglaMamba explores Mamba-based State Space Models (SSMs) as a computationally efficient alternative for Bangla fake news detection. Compared to BanglaBERT and a custom BERT trained from scratch, BanglaMamba achieves a Macro‑F1 score of 0.9029, close to the 0.9057 of the custom BERT, while delivering 2.2× higher inference throughput and 49% lower peak GPU memory usage. Cross‑dataset evaluation shows BanglaBERT generalizes better, underscoring the value of large‑scale pretraining.

arXiv Computation and Language
Oct 12

SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection

SharP is a text‑generation framework that uses both hard and soft prompts to guide large language models in creating task‑specific synthetic news samples for low‑resource fake news detection. The method combines structured factual constraints with learned semantic patterns, and employs a boundary‑aware strategy that focuses on classifier‑uncertain areas to refine decision boundaries. Dual objective optimization balances semantic alignment with source data and classifier feedback, aiming to improve robustness and generalization when labeled data are scarce.

By Ziyu Luo, Wei Li, Haoping Huang, Kecheng Liu, Min Gao
arXiv AI
Oct 5

A Unified BERT-CNN-BiLSTM Framework for Simultaneous Headline Classification and Sentiment Analysis of Bangla News

The paper introduces a hybrid BERT‑CNN‑BiLSTM model for simultaneously classifying Bangla news headlines and performing sentiment analysis. Using the BAN‑ABSA dataset of 9,014 headlines, the authors compare two sampling strategies and report headline accuracy up to 81.37 % and sentiment accuracy up to 73.43 %. The proposed framework outperforms baseline models and establishes new state‑of‑the‑art results for Bangla headline classification and sentiment analysis.

By Mirza Raquib, Munazer Montasir Akash, Tawhid Ahmed, Saydul Akbar Murad, Farida Siddiqi Prity, Mohammad Amzad Hossain, Asif Pervez Polok, Nick Rahimi
arXiv Machine Learning
Aug 24

MIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees

MIL-BERT is a neural network algorithm that classifies large texts by selecting relevant excerpts, inspired by multiple instance learning. It scales to samples with nearly 1 million tokens and has been evaluated on seven datasets, achieving state‑of‑the‑art results on three long‑text tasks such as political bias detection, trigger warning identification, and author demographic inference. The model also generalizes from weakly‑labeled text bags to accurately classify smaller instances.

By John Cadigan, Dayne Freitag, Eric Yeh
arXiv AI
Sep 30

Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?

The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.

By Jiyao Yang, Yang Liu, Zhenyue Qin, Qingyu Chen, Xiuzhen Zhang